MultiObjective Optimization using Evolutionary Computation Techniques (original) (raw)
Related papers
Multi-Objective Optimization Using Evolution Strategies
Facta universitatis. Series electronics and energetics, 2009
The present paper gives an overview of different versions of Evolution Strategies, namely the (1+1) Evolution Strategy, the Higher Order (μ/ρ, λ ) Evolu- tion Strategy and the Niching (κ(μ/ρ, λ)) Evolution Strategy, and how these meth- ods can be applied to problems in Electrical Engineering. Significant features of the algorithms implemented by the authors are presented. Finally, results are discussed on three electromagnetic optimization problems.
A hybrid multiobjective differential evolution method for electromagnetic device optimization
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 2011
Purpose -The purpose of this paper is to show that the performance of differential evolution (DE) can be substantially improved by a combination of techniques. These enhancements are applicable to both single and multiobjective problems. Their combined use allows the optimization of complex 3D electromagnetic devices. Design/methodology/approach -DE is improved by a combination of techniques which are applied in a cascade way and their single and combined effect is tested on well-known benchmarks and domain-specific applications. Findings -It is shown that the combined use of enhancement techniques provides substantial improvements in the speed of convergence for both single and multiobjective problems.
IEEE Transactions on Magnetics, 2000
The differential evolution (DE) algorithm was initially developed for single-objective problems and was shown to be a fast, simple algorithm. In order to utilize these advantages in real-world problems it was adapted for multiobjective global optimization (MOGO) recently. In general multiobjective differential evolutionary algorithm, only use conventional DE strategies, and, in order to optimize performance constrains problems, the feasibility of the solutions was considered only at selection step. This paper presents a new multiobjective evolutionary algorithm based on differential evolution. In the mutation step, the proposed method which applied multiguiders instead of conventional base vector selection method is used. Therefore, feasibility of multiguiders, involving constraint optimization problems, is also considered. Furthermore, the approach also incorporates nondominated sorting method and secondary population for the nondominated solutions. The propose algorithm is compared with resent approaches of multiobjective optimizers in solving multiobjective version of Testing Electromagnetic Analysis Methods (TEAM) problem 22.
Electromagnetic device optimization by hybrid evolution strategy approaches
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 2007
Purpose -This paper aims to show on a widely used benchmark problem that chaotic sequences can improve the search ability of evolution strategies (ES). Design/methodology/approach -The Lozi map is used to generate new individuals in the framework of ES algorithms. A quasi-Newton (QN) method is also used within the iterative loop to improve the solution's quality locally. Findings -It is shown that the combined use of chaotic sequences and QN methods can provide high-quality solutions with small standard deviation on the selected benchmark problem. Research limitations/implications -Although the benchmark is considered to be representative of typical electromagnetic problems, different test cases may give less satisfactory results. Practical implications -The proposed approach appears to be an efficient general purpose optimizer for electromagnetic design problems. Originality/value -This paper introduces the use of chaotic sequences in the area of electromagnetic design optimization.
Higher order evolution strategies for the global optimization of electromagnetic devices
IEEE Transactions on Magnetics, 1993
Abstmct -Basic evolution strategies @S) utilizing simplified features of biological evolution like mutation and selection are applied to solve problems of parameter identification for the optimal design of electromagnetic devices. Due to the fact that objective functions of real world applications usually have more than one minimum, additional features can be added to minimize the risk of getting trapped in a local minimum. Finite life span, recombination and population, applied in @/&) evolution strategies or a "disaster" with an unnatural high stepwidth after a certain number of generations can help to arrive at a reliable solution within a reasonable computational effort.
Multiobjective Optimization in Computational Electromagnetics
In this paper we show how multiobjective optimization can be applied to elec- tromagnetic problems. The optimization algorithms are combined with CAD and mesh generation software, and electromagnetic solvers. Three dieren t multiobjective optimization methods are applied: one evolutionary method, one method based on scalarizing of the objectives combined with a method for single objective optimization and a multiobjective respond surface method. To demonstrate the procedure we study the optimization of the return loss of a patch antennas at two dieren t frequencies.
Niching genetic algorithms for optimization in electromagnetics. I. Fundamentals
IEEE Transactions on Magnetics, 1998
In this paper, we present a new approach for automatic design of electrodes. The investigated method consists in identifying an optimal shape from an optimal equipotential resulting from a system of point charges. The electric field and potential are computed using the point charge simulation method. Niching genetic algorithms and constrained optimization techniques are applied to the electrode benchmark in order to find multiple optimal profiles.
Genetic algorithm optimization applied to electromagnetics: a review
IEEE Transactions on Antennas and Propagation, 1997
Genetic algorithms are on the rise in electromagnetics as design tools and problem solvers because of their versatility and ability to optimize in complex multimodal search spaces. This paper describes the basic genetic algorithm and recounts its history in the electromagnetics literature. Also, the application of advanced genetic operators to the field of electromagnetics is described, and design results are presented for a number of different applications. Index Terms-Genetic algorithms. I. INTRODUCTION D URING the latter half of the nineteenth century, the biological sciences underwent a revolution when Charles Darwin discovered the processes by which nature selects and optimizes organizms fit for life [1]. About the same time, Gregor Mendel learned the basic laws of genetic inheritance which elucidate by what means evolution takes place [2]. The advent of computers and powerful computational techniques now enables us to apply Nature's optimization processes in the form of genetic algorithms (GA's) to devices built using Maxwell's equations. The objective functions that arise in electromagnetic optimization problems are often highly nonlinear, stiff, multiextremal, and nondifferentiable. In addition, they are almost always computationally expensive to evaluate. Historically, the vast majority of research efforts related to the design of electromagnetic systems using optimization methods has relied on deterministic optimization methods (DOM's) [3]. DOM's are known to have important drawbacks when applied to multiextremal and stiff optimization problems [4] and often lead to highly interactive and expensive design procedures. GA's (along with Monte Carlo techniques and simulated annealing) belong to a small but growing class of so-called global optimizers which are stochastic in nature and, therefore, less prone to converge to a weak local optimum than DOM's [5]-[10]. In various forms, GA's have been
IEEE Transactions on Magnetics, 2008
We describe a local search procedure for multiobjective genetic algorithms that employs quadratic approximations for all nonlinear functions involved in the optimization problem. The samples obtained by the algorithm during the evolutionary process are used to fit these quadratic approximations in the neighborhood of the point selected for local search, implying that no extra cost of function evaluations is required. After that, a locally improved solution is easily estimated from the associated quadratic problem. We demonstrate the hybridization of our procedure with the well-known multiobjective genetic algorithm. This methodology can also be coupled with other multiobjective evolutionary algorithms. The results show that the proposed procedure is suitable for time-demanding black-box optimization problems.